Global Navigation Satellite Systems (GNSS) consist of constellations of satellites, wherein each satellite broadcasts signals containing information so that corresponding earth-based receivers that receive the signal can identify the satellite that generated the signal. Based on time of arrival measurements (alternatively expressed as pseudoranges by multiplying by the speed of light) for signals from at least 4 satellites, a GNSS receiver estimates its three-dimensional (3D) location and timing offset from the highly accurate clocks used by the satellites. This is a simple generalization of the concept of trilateration, and a key assumption is that the path from each satellite to the receiver is line of sight (LOS). However, GNSS localization quality is often degraded. This degradation is especially prevalent in urban areas, where the presence of tall buildings generates reflections of the received signals. Because the GNSS location estimate is based, at least in part, on how long it takes the signal to reach the device (i.e., so called “time of flight” measurements), reflections prove especially problematic in determining the GNSS position fix as the time-of-flight, and hence the pseudorange, will increase as a result of the reflection. These errors in pseudorange often lead to large errors in localization, for example, up to 50 meters in high-rise urban environments. Even if the LOS path is available, the pseudorange may be corrupted by the presence of additional reflected paths.
Inaccuracies in GNSS in urban environments have a significant adverse impact in a large, and growing, number of settings. In addition to its traditional applications in transportation logistics, the use of GNSS has become ubiquitous with the advent of consumer mobile electronic devices. GNSS-based localization is relied upon by individual users for both pedestrian and vehicular navigation. Accurate global localization using GNSS also forms the basis for a variety of enterprises such as car services and delivery services. It is also a critical component in vehicular automation technology, with global location using GNSS providing an anchor for fine-grained localization and tracking using vehicular sensors and actuators.
A GNSS receiver has information about the signal-to-noise ratio (SNR) of each satellite it sees, which can be often be obtained via a convenient software interface. These SNRs, employed together with information about the propagation environment, can provide valuable information about location that supplements the standard GNSS position fix. In GNSS and other wireless communication, line-of-sight (LOS) channels are characterized by statistically higher received power levels than those in which the LOS signal component is blocked (e.g., non-LOS or NLOS channels). As a mobile GNSS receiver traverses an area, obstacles (e.g., buildings, trees, terrain) frequently block the LOS component of different satellite signals, resulting in NLOS channels characterized by statistically lower signal-to-noise ratios (SNR). While the NLOS channels cannot be relied upon to determine the position fix of the user device, the decrease in SNR does provide information regarding the location of the device; namely, that the device is within the shadow of a building/infrastructure. Thus, the satellite SNRs yield probabilistic information regarding the receiver's location: higher SNR indicates that the path from the receiver to the satellite is likely LOS, while lower SNR indicates that the path from the receiver to the satellite is likely NLOS. Having knowledge of the layout/map of the urban environment, the satellite SNR signal can be utilized to determine possible locations for the user device based on calculation of positions that would likely be “blocked” or in the shadow of various buildings or structures. Such procedures for extracting location information from satellite SNRs is termed “shadow matching.”
While shadow matching using satellite SNRs provides valuable location information that can improve the standard GNSS location estimate, the information from shadow matching is noisy, and inherently probabilistic. Specifically, high SNR could be obtained for NLOS paths due to strong reflections, while low SNR could be obtained for LOS paths due to multipath interference. Thus, deterministic shadow matching does not work in complex propagation environments.
It would therefore be beneficial to develop a robust, computationally efficient approach for utilizing shadow matching for localization and tracking, in a manner that accounts for modeling uncertainties and measurement noise.